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Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China
BACKGROUND: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308895/ https://www.ncbi.nlm.nih.gov/pubmed/35871653 http://dx.doi.org/10.1186/s12879-022-07620-y |
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author | Wu, Ziwei Chen, Ziyi Long, Siyu Wu, Aiping Wang, Hongsheng |
author_facet | Wu, Ziwei Chen, Ziyi Long, Siyu Wu, Aiping Wang, Hongsheng |
author_sort | Wu, Ziwei |
collection | PubMed |
description | BACKGROUND: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis. METHODS: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020. RESULTS: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions. CONCLUSIONS: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07620-y. |
format | Online Article Text |
id | pubmed-9308895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93088952022-07-25 Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China Wu, Ziwei Chen, Ziyi Long, Siyu Wu, Aiping Wang, Hongsheng BMC Infect Dis Research BACKGROUND: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis. METHODS: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020. RESULTS: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions. CONCLUSIONS: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07620-y. BioMed Central 2022-07-24 /pmc/articles/PMC9308895/ /pubmed/35871653 http://dx.doi.org/10.1186/s12879-022-07620-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wu, Ziwei Chen, Ziyi Long, Siyu Wu, Aiping Wang, Hongsheng Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title | Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title_full | Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title_fullStr | Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title_full_unstemmed | Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title_short | Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China |
title_sort | incidence of pulmonary tuberculosis under the regular covid-19 epidemic prevention and control in china |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308895/ https://www.ncbi.nlm.nih.gov/pubmed/35871653 http://dx.doi.org/10.1186/s12879-022-07620-y |
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